R Notebook

library(ggplot2)
library(gridExtra)
library(GGally)
library(corrplot)
library(dplyr)
library(tidyr)
library(viridis)
library(reshape2)

# Configuración general
theme_set(theme_minimal(base_size = 11))

# NOTA: Reemplazar 'ckd_data' con el nombre real de tu dataframe
getwd()  

[1] "/Users/samircabrera/Development/Universidad/Inteligencia Artificial/Inteligencia-Artificial/Proyecto/Code/plots"

setwd("/Users/samircabrera/Development/Universidad/Inteligencia Artificial/Inteligencia-Artificial")  
ckd_data <- read.csv("/Users/samircabrera/Development/Universidad/Inteligencia Artificial/Inteligencia-Artificial/Proyecto/Dataset/Chronic_Kidney_Dsease_data.csv")
head(datos)

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```r
plot_correlation_matrix <- function(data) {
  # Seleccionar solo variables numéricas relevantes
  numeric_vars <- data %>% 
    select(Age, BMI, SystolicBP, DiastolicBP, FastingBloodSugar, HbA1c,
           SerumCreatinine, BUNLevels, GFR, ProteinInUrine, ACR,
           HemoglobinLevels, CholesterolTotal, CholesterolLDL,
           FatigueLevels, QualityOfLifeScore, MedicationAdherence)
  
  cor_matrix <- cor(numeric_vars, use = \complete.obs\)
  
  corrplot(cor_matrix, 
           method = \color\,
           type = \upper\,
           order = \hclust\,  # Agrupación jerárquica
           addrect = 4,       # Marca 4 clusters principales
           tl.col = \black\,
           tl.srt = 45,
           tl.cex = 0.8,
           col = colorRampPalette(c(\#6D9EC1\, \white\, \#E46726\))(200),
           title = \Matriz de Correlación con Clustering Jerárquico\,
           mar = c(0,0,2,0))
}
```

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```r
plot_correlation_matrix <- function(data) {
  # Seleccionar solo variables numéricas relevantes
  numeric_vars <- data %>% 
    select(Age, BMI, SystolicBP, DiastolicBP, FastingBloodSugar, HbA1c,
           SerumCreatinine, BUNLevels, GFR, ProteinInUrine, ACR,
           HemoglobinLevels, CholesterolTotal, CholesterolLDL,
           FatigueLevels, QualityOfLifeScore, MedicationAdherence)
  
  cor_matrix <- cor(numeric_vars, use = \complete.obs\)
  
  corrplot(cor_matrix, 
           method = \color\,
           type = \upper\,
           order = \hclust\,  # Agrupación jerárquica
           addrect = 4,       # Marca 4 clusters principales
           tl.col = \black\,
           tl.srt = 45,
           tl.cex = 0.8,
           col = colorRampPalette(c(\#6D9EC1\, \white\, \#E46726\))(200),
           title = \Matriz de Correlación con Clustering Jerárquico\,
           mar = c(0,0,2,0))
}

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plot_renal_pairs <- function(data) {
  renal_data <- data %>%
    select(GFR, SerumCreatinine, BUNLevels, ProteinInUrine, ACR, Diagnosis) %>%
    mutate(Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\)))
  
  ggpairs(renal_data,
          columns = 1:5,
          aes(color = Diagnosis, alpha = 0.5),
          upper = list(continuous = wrap(\cor\, size = 3)),
          lower = list(continuous = wrap(\points\, alpha = 0.3, size = 0.5)),
          diag = list(continuous = wrap(\densityDiag\, alpha = 0.5)),
          title = \Relaciones entre Marcadores Renales (por Diagnosis)\) +
    theme(plot.title = element_text(hjust = 0.5, face = \bold\))
}

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```r
```r
plot_renal_pairs <- function(data) {
  renal_data <- data %>%
    select(GFR, SerumCreatinine, BUNLevels, ProteinInUrine, ACR, Diagnosis) %>%
    mutate(Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\)))
  
  ggpairs(renal_data,
          columns = 1:5,
          aes(color = Diagnosis, alpha = 0.5),
          upper = list(continuous = wrap(\cor\, size = 3)),
          lower = list(continuous = wrap(\points\, alpha = 0.3, size = 0.5)),
          diag = list(continuous = wrap(\densityDiag\, alpha = 0.5)),
          title = \Relaciones entre Marcadores Renales (por Diagnosis)\) +
    theme(plot.title = element_text(hjust = 0.5, face = \bold\))
}
```
```

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plot_gfr_3d <- function(data) {
  data_plot <- data %>%
    mutate(Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\)))
  
  ggplot(data_plot, aes(x = SerumCreatinine, y = GFR)) +
    geom_point(aes(color = Age, size = BMI, shape = Diagnosis), alpha = 0.6) +
    scale_color_viridis(option = \plasma\) +
    scale_size_continuous(range = c(1, 6)) +
    geom_smooth(aes(linetype = Diagnosis), method = \loess\, se = TRUE, 
                color = \black\, size = 0.8) +
    labs(title = \GFR vs Creatinina (Edad en color

```r
plot_gfr_3d <- function(data) {
  data_plot <- data %>%
    mutate(Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\)))
  
  ggplot(data_plot, aes(x = SerumCreatinine, y = GFR)) +
    geom_point(aes(color = Age, size = BMI, shape = Diagnosis), alpha = 0.6) +
    scale_color_viridis(option = \plasma\) +
    scale_size_continuous(range = c(1, 6)) +
    geom_smooth(aes(linetype = Diagnosis), method = \loess\, se = TRUE, 
                color = \black\, size = 0.8) +
    labs(title = \GFR vs Creatinina (Edad en color

<!-- rnb-source-begin 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 -->

```r
plot_clinical_heatmap <- function(data) {
  # Crear estadios de CKD basados en GFR
  data_staged <- data %>%
    mutate(CKD_Stage = case_when(
      GFR >= 90 ~ \Normal (≥90)\,
      GFR >= 60 ~ \Mild (60-89)\,
      GFR >= 30 ~ \Moderate (30-59)\,
      GFR >= 15 ~ \Severe (15-29)\,
      TRUE ~ \Kidney Failure (<15)\
    )) %>%
    mutate(CKD_Stage = factor(CKD_Stage, 
                              levels = c(\Normal (≥90)\, \Mild (60-89)\, 
                                         \Moderate (30-59)\, \Severe (15-29)\, 
                                         \Kidney Failure (<15)\)))
  
  # Calcular promedios por estadio
  heatmap_data <- data_staged %>%
    group_by(CKD_Stage) %>%
    summarise(
      BMI = mean(BMI, na.rm = TRUE),
      SystolicBP = mean(SystolicBP, na.rm = TRUE),
      HbA1c = mean(HbA1c, na.rm = TRUE),
      Creatinine = mean(SerumCreatinine, na.rm = TRUE),
      Hemoglobin = mean(HemoglobinLevels, na.rm = TRUE),
      ProteinUrine = mean(ProteinInUrine, na.rm = TRUE),
      Fatigue = mean(FatigueLevels, na.rm = TRUE),
      QoL = mean(QualityOfLifeScore, na.rm = TRUE)
    ) %>%
    pivot_longer(-CKD_Stage, names_to = \Variable\, values_to = \Value\) %>%
    group_by(Variable) %>%
    mutate(Value_scaled = scale(Value)[,1])  # Estandarizar por variable
  
  ggplot(heatmap_data, aes(x = CKD_Stage, y = Variable, fill = Value_scaled)) +
    geom_tile(color = \white\, size = 0.5) +
    geom_text(aes(label = round(Value, 1)), size = 3, color = \white\) +
    scale_fill_gradient2(low = \#3B9AB2\, mid = \#EBCC2A\, high = \#F21A00\,
                         midpoint = 0, name = \Z-score\) +
    labs(title = \Perfil Clínico Promedio por Estadio de CKD\,
         x = \Estadio de Enfermedad Renal\,
         y = \Variable Clínica\,
         caption = \Valores estandarizados (Z-scores) - números = valor real promedio\) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1),
          plot.title = element_text(hjust = 0.5, face = \bold\))
}
```

<!-- rnb-source-end -->

```r
plot_clinical_heatmap <- function(data) {
  # Crear estadios de CKD basados en GFR
  data_staged <- data %>%
    mutate(CKD_Stage = case_when(
      GFR >= 90 ~ \Normal (≥90)\,
      GFR >= 60 ~ \Mild (60-89)\,
      GFR >= 30 ~ \Moderate (30-59)\,
      GFR >= 15 ~ \Severe (15-29)\,
      TRUE ~ \Kidney Failure (<15)\
    )) %>%
    mutate(CKD_Stage = factor(CKD_Stage, 
                              levels = c(\Normal (≥90)\, \Mild (60-89)\, 
                                         \Moderate (30-59)\, \Severe (15-29)\, 
                                         \Kidney Failure (<15)\)))
  
  # Calcular promedios por estadio
  heatmap_data <- data_staged %>%
    group_by(CKD_Stage) %>%
    summarise(
      BMI = mean(BMI, na.rm = TRUE),
      SystolicBP = mean(SystolicBP, na.rm = TRUE),
      HbA1c = mean(HbA1c, na.rm = TRUE),
      Creatinine = mean(SerumCreatinine, na.rm = TRUE),
      Hemoglobin = mean(HemoglobinLevels, na.rm = TRUE),
      ProteinUrine = mean(ProteinInUrine, na.rm = TRUE),
      Fatigue = mean(FatigueLevels, na.rm = TRUE),
      QoL = mean(QualityOfLifeScore, na.rm = TRUE)
    ) %>%
    pivot_longer(-CKD_Stage, names_to = \Variable\, values_to = \Value\) %>%
    group_by(Variable) %>%
    mutate(Value_scaled = scale(Value)[,1])  # Estandarizar por variable
  
  ggplot(heatmap_data, aes(x = CKD_Stage, y = Variable, fill = Value_scaled)) +
    geom_tile(color = \white\, size = 0.5) +
    geom_text(aes(label = round(Value, 1)), size = 3, color = \white\) +
    scale_fill_gradient2(low = \#3B9AB2\, mid = \#EBCC2A\, high = \#F21A00\,
                         midpoint = 0, name = \Z-score\) +
    labs(title = \Perfil Clínico Promedio por Estadio de CKD\,
         x = \Estadio de Enfermedad Renal\,
         y = \Variable Clínica\,
         caption = \Valores estandarizados (Z-scores) - números = valor real promedio\) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1),
          plot.title = element_text(hjust = 0.5, face = \bold\))
}

<!-- rnb-source-end -->


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<!-- rnb-text-begin -->



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<!-- rnb-output-begin 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-->

plot_parallel_coordinates <- function(data) {
  parallel_data <- data %>%
    select(PatientID, BMI, PhysicalActivity, DietQuality, SleepQuality,
           AlcoholConsumption, Smoking, Diagnosis) %>%
    mutate(Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\))) %>%
    # Estandarizar variables para escala común
    mutate(across(c(BMI, PhysicalActivity, DietQuality, SleepQuality, 
                    AlcoholConsumption), scale)) %>%
    sample_n(200)  # Muestra para claridad visual
  
  parallel_long <- parallel_data %>%
    pivot_longer(cols = c(BMI, PhysicalActivity, DietQuality, 
                          SleepQuality, AlcoholConsumption),
                 names_to = \Variable\, values_to = \Value\) %>%
    mutate(Variable = factor(Variable, 
                             levels = c(\BMI\, \PhysicalActivity\, \DietQuality\,
                                        \SleepQuality\, \AlcoholConsumption\)))
  
  ggplot(parallel_long, aes(x = Variable, y = Value, group = PatientID)) +
    geom_line(aes(color = Diagnosis), alpha = 0.3, size = 0.5) +
    geom_point(aes(color = Diagnosis), alpha = 0.4, size = 1) +
    scale_color_manual(values = c(\No CKD\ = \#00BA38\, \CKD\ = \#F8766D\)) +
    labs(title = \Perfiles de Estilo de Vida (Parallel Coordinates)\,
         subtitle = \Muestra de 200 pacientes - Variables estandarizadas\,
         y = \Valor Estandarizado (Z-score)\,
         x = \\) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1),
          plot.title = element_text(hjust = 0.5, face = \bold\),
          plot.subtitle = element_text(hjust = 0.5))
}

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```r
```r
plot_parallel_coordinates <- function(data) {
  parallel_data <- data %>%
    select(PatientID, BMI, PhysicalActivity, DietQuality, SleepQuality,
           AlcoholConsumption, Smoking, Diagnosis) %>%
    mutate(Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\))) %>%
    # Estandarizar variables para escala común
    mutate(across(c(BMI, PhysicalActivity, DietQuality, SleepQuality, 
                    AlcoholConsumption), scale)) %>%
    sample_n(200)  # Muestra para claridad visual
  
  parallel_long <- parallel_data %>%
    pivot_longer(cols = c(BMI, PhysicalActivity, DietQuality, 
                          SleepQuality, AlcoholConsumption),
                 names_to = \Variable\, values_to = \Value\) %>%
    mutate(Variable = factor(Variable, 
                             levels = c(\BMI\, \PhysicalActivity\, \DietQuality\,
                                        \SleepQuality\, \AlcoholConsumption\)))
  
  ggplot(parallel_long, aes(x = Variable, y = Value, group = PatientID)) +
    geom_line(aes(color = Diagnosis), alpha = 0.3, size = 0.5) +
    geom_point(aes(color = Diagnosis), alpha = 0.4, size = 1) +
    scale_color_manual(values = c(\No CKD\ = \#00BA38\, \CKD\ = \#F8766D\)) +
    labs(title = \Perfiles de Estilo de Vida (Parallel Coordinates)\,
         subtitle = \Muestra de 200 pacientes - Variables estandarizadas\,
         y = \Valor Estandarizado (Z-score)\,
         x = \\) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1),
          plot.title = element_text(hjust = 0.5, face = \bold\),
          plot.subtitle = element_text(hjust = 0.5))
}
```
```

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plot_demographic_boxplots <- function(data) {
  data_demo <- data %>%
    mutate(
      Gender = factor(Gender, labels = c(\Male\, \Female\)),
      Ethnicity = factor(Ethnicity, labels = c(\Caucasian\, \African American\, 
                                               \Asian\, \Other\)),
      SocioeconomicStatus = factor(SocioeconomicStatus, 
                                   labels = c(\Low\, \Middle\, \High\))
    ) %>%
    select(Gender, Ethnicity, SocioeconomicStatus, GFR, HbA1c, 
           SystolicBP, QualityOfLifeScore) %>%
    pivot_longer(cols = c(GFR, HbA1c, SystolicBP, QualityOfLifeScore),
                 names_to = \Biomarker\, values_to = \Value\)
  
  ggplot(data_demo, aes(x = SocioeconomicStatus, y = Value, fill = Gender)) +
    geom_boxplot(alpha = 0.7, outlier.size = 0.5) +
    facet_grid(Biomarker ~ Ethnicity, scales = \free_y\) +
    scale_fill_brewer(palette = \Set2\) +
    labs(title = \Biomarcadores por Demografía (Género

```r
plot_demographic_boxplots <- function(data) {
  data_demo <- data %>%
    mutate(
      Gender = factor(Gender, labels = c(\Male\, \Female\)),
      Ethnicity = factor(Ethnicity, labels = c(\Caucasian\, \African American\, 
                                               \Asian\, \Other\)),
      SocioeconomicStatus = factor(SocioeconomicStatus, 
                                   labels = c(\Low\, \Middle\, \High\))
    ) %>%
    select(Gender, Ethnicity, SocioeconomicStatus, GFR, HbA1c, 
           SystolicBP, QualityOfLifeScore) %>%
    pivot_longer(cols = c(GFR, HbA1c, SystolicBP, QualityOfLifeScore),
                 names_to = \Biomarker\, values_to = \Value\)
  
  ggplot(data_demo, aes(x = SocioeconomicStatus, y = Value, fill = Gender)) +
    geom_boxplot(alpha = 0.7, outlier.size = 0.5) +
    facet_grid(Biomarker ~ Ethnicity, scales = \free_y\) +
    scale_fill_brewer(palette = \Set2\) +
    labs(title = \Biomarcadores por Demografía (Género

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```r
plot_qol_bubble <- function(data) {
  bubble_data <- data %>%
    mutate(
      Severity_Score = (SerumCreatinine - min(SerumCreatinine)) / 
        (max(SerumCreatinine) - min(SerumCreatinine)) * 10,
      Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\))
    )
  
  ggplot(bubble_data, aes(x = FatigueLevels, y = QualityOfLifeScore)) +
    geom_point(aes(size = Severity_Score, color = GFR, shape = Diagnosis), 
               alpha = 0.6) +
    scale_color_viridis(option = \magma\, direction = -1, 
                        name = \GFR\n(mL/min/1.73m²)\) +
    scale_size_continuous(range = c(1, 10), 
                          name = \Severidad\n(Creatinina)\) +
    geom_smooth(aes(color = NULL, size = NULL), method = \lm\, 
                se = TRUE, color = \black\, linetype = \dashed\) +
    labs(title = \Calidad de Vida vs Fatiga (tamaño = Severidad

```r
plot_qol_bubble <- function(data) {
  bubble_data <- data %>%
    mutate(
      Severity_Score = (SerumCreatinine - min(SerumCreatinine)) / 
        (max(SerumCreatinine) - min(SerumCreatinine)) * 10,
      Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\))
    )
  
  ggplot(bubble_data, aes(x = FatigueLevels, y = QualityOfLifeScore)) +
    geom_point(aes(size = Severity_Score, color = GFR, shape = Diagnosis), 
               alpha = 0.6) +
    scale_color_viridis(option = \magma\, direction = -1, 
                        name = \GFR\n(mL/min/1.73m²)\) +
    scale_size_continuous(range = c(1, 10), 
                          name = \Severidad\n(Creatinina)\) +
    geom_smooth(aes(color = NULL, size = NULL), method = \lm\, 
                se = TRUE, color = \black\, linetype = \dashed\) +
    labs(title = \Calidad de Vida vs Fatiga (tamaño = Severidad

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```r
plot_medication_violins <- function(data) {
  med_data <- data %>%
    mutate(
      Treatment_Intensity = ACEInhibitors + Diuretics + Statins + 
        AntidiabeticMedications,
      Treatment_Group = case_when(
        Treatment_Intensity == 0 ~ \No Meds\,
        Treatment_Intensity <= 2 ~ \Low (1-2)\,
        TRUE ~ \High (3-4)\
      ),
      Treatment_Group = factor(Treatment_Group, 
                               levels = c(\No Meds\, \Low (1-2)\, \High (3-4)\))
    ) %>%
    select(Treatment_Group, GFR, HbA1c, SystolicBP, QualityOfLifeScore) %>%
    pivot_longer(cols = c(GFR, HbA1c, SystolicBP, QualityOfLifeScore),
                 names_to = \Outcome\, values_to = \Value\)
  
  ggplot(med_data, aes(x = Treatment_Group, y = Value, fill = Treatment_Group)) +
    geom_violin(alpha = 0.7, trim = FALSE) +
    geom_boxplot(width = 0.2, alpha = 0.8, outlier.size = 0.5) +
    facet_wrap(~ Outcome, scales = \free_y\, ncol = 2) +
    scale_fill_brewer(palette = \YlOrRd\) +
    labs(title = \Distribución de Outcomes Clínicos por Intensidad de Tratamiento\,
         subtitle = \Intensidad = suma de medicamentos clave (ACEi

```r
plot_medication_violins <- function(data) {
  med_data <- data %>%
    mutate(
      Treatment_Intensity = ACEInhibitors + Diuretics + Statins + 
        AntidiabeticMedications,
      Treatment_Group = case_when(
        Treatment_Intensity == 0 ~ \No Meds\,
        Treatment_Intensity <= 2 ~ \Low (1-2)\,
        TRUE ~ \High (3-4)\
      ),
      Treatment_Group = factor(Treatment_Group, 
                               levels = c(\No Meds\, \Low (1-2)\, \High (3-4)\))
    ) %>%
    select(Treatment_Group, GFR, HbA1c, SystolicBP, QualityOfLifeScore) %>%
    pivot_longer(cols = c(GFR, HbA1c, SystolicBP, QualityOfLifeScore),
                 names_to = \Outcome\, values_to = \Value\)
  
  ggplot(med_data, aes(x = Treatment_Group, y = Value, fill = Treatment_Group)) +
    geom_violin(alpha = 0.7, trim = FALSE) +
    geom_boxplot(width = 0.2, alpha = 0.8, outlier.size = 0.5) +
    facet_wrap(~ Outcome, scales = \free_y\, ncol = 2) +
    scale_fill_brewer(palette = \YlOrRd\) +
    labs(title = \Distribución de Outcomes Clínicos por Intensidad de Tratamiento\,
         subtitle = \Intensidad = suma de medicamentos clave (ACEi

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```r
plot_density_contour <- function(data) {
  data_contour <- data %>%
    mutate(Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\)))
  
  ggplot(data_contour, aes(x = HbA1c, y = GFR)) +
    stat_density_2d(aes(fill = after_stat(level)), geom = \polygon\, alpha = 0.5) +
    geom_point(aes(color = Diagnosis), alpha = 0.3, size = 1) +
    scale_fill_viridis(option = \cividis\, name = \Densidad\) +
    scale_color_manual(values = c(\No CKD\ = \#00BA38\, \CKD\ = \#F8766D\)) +
    geom_hline(yintercept = 60, linetype = \dashed\, color = \red\, size = 0.8) +
    annotate(\text\, x = 9, y = 65, label = \GFR = 60 (umbral CKD)\, 
             color = \red\, size = 3) +
    labs(title = \Contorno de Densidad: GFR vs HbA1c\,
         subtitle = \Identificación de clusters de riesgo metabólico-renal\,
         x = \HbA1c (%)\,
         y = \GFR (mL/min/1.73m²)\) +
    theme(plot.title = element_text(hjust = 0.5, face = \bold\),
          plot.subtitle = element_text(hjust = 0.5))
}
```

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```r
plot_density_contour <- function(data) {
  data_contour <- data %>%
    mutate(Diagnosis = factor(Diagnosis, labels = c(\No CKD\, \CKD\)))
  
  ggplot(data_contour, aes(x = HbA1c, y = GFR)) +
    stat_density_2d(aes(fill = after_stat(level)), geom = \polygon\, alpha = 0.5) +
    geom_point(aes(color = Diagnosis), alpha = 0.3, size = 1) +
    scale_fill_viridis(option = \cividis\, name = \Densidad\) +
    scale_color_manual(values = c(\No CKD\ = \#00BA38\, \CKD\ = \#F8766D\)) +
    geom_hline(yintercept = 60, linetype = \dashed\, color = \red\, size = 0.8) +
    annotate(\text\, x = 9, y = 65, label = \GFR = 60 (umbral CKD)\, 
             color = \red\, size = 3) +
    labs(title = \Contorno de Densidad: GFR vs HbA1c\,
         subtitle = \Identificación de clusters de riesgo metabólico-renal\,
         x = \HbA1c (%)\,
         y = \GFR (mL/min/1.73m²)\) +
    theme(plot.title = element_text(hjust = 0.5, face = \bold\),
          plot.subtitle = element_text(hjust = 0.5))
}

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plot_age_ridges <- function(data) {
  library(ggridges)
  
  ridge_data <- data %>%
    mutate(
      Comorbidity_Profile = case_when(
        FamilyHistoryDiabetes == 1 & FamilyHistoryHypertension == 1 ~ 
          \Diabetes + HTN\,
        FamilyHistoryDiabetes == 1 ~ \Diabetes Only\,
        FamilyHistoryHypertension == 1 ~ \HTN Only\,
        FamilyHistoryKidneyDisease == 1 ~ \Kidney Disease\,
        TRUE ~ \None\
      )
    ) %>%
    filter(Comorbidity_Profile != \None\)
  
  ggplot(ridge_data, aes(x = Age, y = Comorbidity_Profile, fill = Comorbidity_Profile)) +
    geom_density_ridges(alpha = 0.7, scale = 1.5) +
    scale_fill_viridis(discrete = TRUE, option = \turbo\) +
    labs(title = \Distribución de Edad según Historia Familiar de Comorbilidades\,
         x = \Edad (años)\,
         y = \Perfil de Comorbilidad Familiar\) +
    theme_ridges() +
    theme(legend.position = \none\,
          plot.title = element_text(hjust = 0.5, face = \bold\))
}

````

```r
plot_age_ridges <- function(data) {
  library(ggridges)
  
  ridge_data <- data %>%
    mutate(
      Comorbidity_Profile = case_when(
        FamilyHistoryDiabetes == 1 & FamilyHistoryHypertension == 1 ~ 
          \Diabetes + HTN\,
        FamilyHistoryDiabetes == 1 ~ \Diabetes Only\,
        FamilyHistoryHypertension == 1 ~ \HTN Only\,
        FamilyHistoryKidneyDisease == 1 ~ \Kidney Disease\,
        TRUE ~ \None\
      )
    ) %>%
    filter(Comorbidity_Profile != \None\)
  
  ggplot(ridge_data, aes(x = Age, y = Comorbidity_Profile, fill = Comorbidity_Profile)) +
    geom_density_ridges(alpha = 0.7, scale = 1.5) +
    scale_fill_viridis(discrete = TRUE, option = \turbo\) +
    labs(title = \Distribución de Edad según Historia Familiar de Comorbilidades\,
         x = \Edad (años)\,
         y = \Perfil de Comorbilidad Familiar\) +
    theme_ridges() +
    theme(legend.position = \none\,
          plot.title = element_text(hjust = 0.5, face = \bold\))
}

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```r
generate_all_plots <- function(data) {
  # Crear lista para almacenar plots
  plots <- list()
  
  cat("Generando visualizaciones multivariables...\n\n")
  
  cat("1. Matriz de Correlación con Clustering...\n")
  plot_correlation_matrix(data)
  
  cat("2. Pairplot de Variables Renales...\n")
  plots$renal_pairs <- plot_renal_pairs(data)
  print(plots$renal_pairs)
  
  cat("3. Scatterplot 4D (GFR-Creatinina-Edad-BMI)...\n")
  plots$gfr_3d <- plot_gfr_3d(data)
  print(plots$gfr_3d)
  
  cat("4. Heatmap de Perfiles Clínicos...\n")
  plots$heatmap <- plot_clinical_heatmap(data)
  print(plots$heatmap)
  
  cat("5. Parallel Coordinates (Factores de Riesgo)...\n")
  plots$parallel <- plot_parallel_coordinates(data)
  print(plots$parallel)
  
  cat("6. Boxplots Demográficos Facetados...\n")
  plots$demo_box <- plot_demographic_boxplots(data)
  print(plots$demo_box)
  
  cat("7. Bubble Chart (Calidad de Vida)...\n")
  plots$bubble <- plot_qol_bubble(data)
  print(plots$bubble)
  
  cat("8. Violin Plots (Medicamentos)...\n")
  plots$violins <- plot_medication_violins(data)
  print(plots$violins)
  
  cat("9. Contour Plot (Densidad GFR-HbA1c)...\n")
  plots$contour <- plot_density_contour(data)
  print(plots$contour)
  
  cat("10. Ridge Plot (Edad por Comorbilidades)...\n")
  plots$ridges <- plot_age_ridges(data)
  print(plots$ridges)
  
  cat("\n¡Todas las visualizaciones generadas exitosamente!\n")
  
  return(invisible(plots))
}

plots <- generate_all_plots(ckd_data)

Generando visualizaciones multivariables...

1. Matriz de Correlación con Clustering...
2. Pairplot de Variables Renales...
3. Scatterplot 4D (GFR-Creatinina-Edad-BMI)...
4. Heatmap de Perfiles Clínicos...
5. Parallel Coordinates (Factores de Riesgo)...
6. Boxplots Demográficos Facetados...
7. Bubble Chart (Calidad de Vida)...
8. Violin Plots (Medicamentos)...
9. Contour Plot (Densidad GFR-HbA1c)...
10. Ridge Plot (Edad por Comorbilidades)...

¡Todas las visualizaciones generadas exitosamente!